Morphological variations analysis of five different populations of Scapharca subcrenata in China
Ying-Zhu Rao
Abstract
Ying-Zhu Rao
Abstract
Based on 10 morphological characters of populations of Scapharca subcrenata ,from Shandong, Tianjin, Guangdong, Hainan and Guangxi, multivariate morphometrics were used to investigate their morphological variations among the five different geographical populations. The results of cluster analysis and principal component analysis showed that the populations of Scapharca subcrenata form Tianjin Tanggu and Shandong Qingdao were rather similar in morphology, whereas Guangxi Beihai population different form other populations in morphology. The principal component analysis resulted in three principal components. The contributory ratios of the three principal components were 34.70 %, 19.80 % and 15.00 % respectively, and the cumulative contributory ratio was 69.50 %. The result of stepwise discriminant analysis revealed that the five populations differed significantly in morphology (P0.01). The discriminant functions of five populations were established, and the discriminant accuracy was 45.45 %~95.45 % for P1 and 36.36 %~95.45 % for P2. The average discriminant accuracy was 74.50 %.
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Based on 10 morphological characters of populations of Scapharca subcrenata ,from Shandong, Tianjin, Guangdong, Hainan and Guangxi, multivariate morphometrics were used to investigate their morphological variations among the five different geographical populations. The results of cluster analysis and principal component analysis showed that the populations of Scapharca subcrenata form Tianjin Tanggu and Shandong Qingdao were rather similar in morphology, whereas Guangxi Beihai population different form other populations in morphology. The principal component analysis resulted in three principal components. The contributory ratios of the three principal components were 34.70 %, 19.80 % and 15.00 % respectively, and the cumulative contributory ratio was 69.50 %. The result of stepwise discriminant analysis revealed that the five populations differed significantly in morphology (P0.01). The discriminant functions of five populations were established, and the discriminant accuracy was 45.45 %~95.45 % for P1 and 36.36 %~95.45 % for P2. The average discriminant accuracy was 74.50 %.
Key concepts: Principal component analysis, Morphometrics, Biology, Morphological analysis, Linear discriminant analysis, Morphology (biology), Population, Multivariate statistics